오피니언 마이닝과 네트워크 분석을 활용한 상품 커뮤니티 분석: 영화 흥행성과 예측 사례

ABSTRACTPurposeThis study examined how social network strength (i.e., the intensity and frequency of interactions within one's personal network, assessed using centrality measures such as degree, closeness, betweenness, and eigenvector centrality) affects mental health.MethodsThe study was conducted with 108 first‐year university students. Data were collected using a structured questionnaire, and NetMiner 4.0 was employed to analyze degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality as indicators of social network centrality. Additionally, Quadratic Assignment Procedure (QAP) correlation analysis and QAP regression analysis were conducted to assess the relationship between social network strength and mental health.ResultsThe study found that stronger social networks were linked to better mental health in late adolescence. Specifically, higher social network centrality was associated with lower levels of hopelessness and depression. QAP correlation analysis revealed that hopelessness had a significant negative correlation with in‐closeness centrality (which reflects how easily one can be reached by others within the network) (r = −0.252, p < 0.05), while depression was negatively correlated with in‐degree centrality (the number of direct incoming connections from peers) (r = −0.233, p < 0.05), in‐closeness centrality (r = −0.256, p < 0.05), and eigenvector centrality (a measure of how well a person is connected to popular or influential members of the network) (r = −0.291, p < 0.01). QAP regression analysis further confirmed that weaker social ties, indicated by higher out‐closeness centrality (how easily one can reach others, especially those at a distance), were associated with higher depressive symptoms (adj. R2 = 0.143, F = 2.423, p < 0.05). These results suggest that building stronger and more integrated social networks may help reduce psychological distress and support mental well‐being in late adolescence.ConclusionStronger social networks in late adolescence are associated with better mental health, highlighting the importance of social connections. While correlational findings suggest general links between centrality and emotional well‐being, the regression results underscore specific predictors with practical implications. In‐degree, in‐closeness, and eigenvector centrality were associated with reduced depressive symptoms, indicating that being well‐integrated within a social network may offer protective benefits. In contrast, high out‐closeness centrality predicted increased depression, suggesting that the effort required to maintain outward‐reaching connections may impose psychological burdens. These findings suggest that targeted interventions should focus on strengthening inward social integration while addressing the stress related to maintaining extensive outward connections, providing direction for more effective youth mental health strategies.

The movie's online word of mouth (WOM) currently is a vital factor affecting the movie box office. To prove this relationship in empirical way, this paper selects 150 released movies in mainland China as the research sample and the movie box office, volume of WOM and valence of WOM are the main variables. Granger causality test and simultaneous equation model are applied as the main method. Combining the theory with the empirical study results, the following conclusions can be made. First, the volume significant promoted the box office Second, in the first week after the film is released, the valence of WOM, so as the box office does. Second, in the first week after the film is released, the valence of WOM will inhibit movie box office significantly. However, in the second week, this effect is no longer significant.

Centrality, which quantifies the importance of individual nodes, is among the most essential concepts in modern network theory. As there are many ways in which a node can be important, many different centrality measures are in use. Here, we concentrate on versions of the common betweenness and closeness centralities. The former measures the fraction of paths between pairs of nodes that go through a given node, while the latter measures an average inverse distance between a particular node and all other nodes. Both centralities only consider shortest paths (i.e., geodesics) between pairs of nodes. Here we develop a method, based on absorbing Markov chains, that enables us to continuously interpolate both of these centrality measures away from the geodesic limit and toward a limit where no restriction is placed on the length of the paths the walkers can explore. At this second limit, the interpolated betweenness and closeness centralities reduce, respectively, to the well-known current-betweenness and resistance-closeness (information) centralities. The method is tested numerically on four real networks, revealing complex changes in node centrality rankings with respect to the value of the interpolation parameter. Nonmonotonic betweenness behaviors are found to characterize nodes that lie close to intercommunity boundaries in the studied networks.

Calculating centrality measures has been used to identify Cyber-physical System critical component for some time. However, accurate identification of critical elements within the interconnected system that are vulnerable to system failures and intentional attacks requires not only a reliable model but robust index to dynamic system operating environment. These measures include 1) Node Degree, 2) Node Importance, 3) Betweenness Centrality, 4) Closeness Centrality and 5) Eigenvector Centrality. System component criticalities have been evaluated based on these measures in this paper and their performances and rankings under different network topologies and operating conditions have been compared using 1) Pearson Correlation Coefficient and 2) Spearman Correlation Coefficient. This has enabled the quantification of the optimality of using different centrality measures in different network scenarios. The results suggest that Betweenness and Degree centralities are the most suitable indices for identifying critical electrical power system bus and cyber system node individually. Moreover, the degree of correlation between directed measures has suggested also the necessity of using directional model in Cyber-physical System studies.

Brinkmann graph is a 4-regular graph with 21 nodes and 42 edges discovered by Gunnar Brinkmann in 1992. To our knowledge, the research specifically on Brinkmann graph is still hard to find. Therefore, this research was carried out to analyze the Brinkmann graph in term of its centrality. The centrality measures used are degree, betweenness, and closeness centrality. In this paper, we presented the centrality measures not only on the Brinkmann graph but also on the Brinkmann graph after node deletion to see how the impact of node deletion to the centrality of graph. Before deletion, the results showed that according to the betweenness centrality, there exist 7 nodes who act as mediators or bridges in the Brinkmann graph. Therefore, when a node among these nodes has deleted, it affected not only any other mediator nodes and the furthest nodes from the deleted node but also the nodes that are adjacent to the deleted nodes.

Co-authorship networks and research impact: A social capital perspective

Preventing power transmission failures in its network of electrical terminal stations is a major concern for the infrastructure of the Thai capital Bangkok and its vicinity. Towards this objective the present study aims to analyse the network and its reliability under conditions of increased demand. The analysis is based on a representation of the network as a graph allowing to identify the most important terminal stations by graph-theoretical terms. These are, in particular, the centrality measures Degree Centrality (DC) giving the number of a station’s one-hop neighbours, Closeness Centrality (CC) describing the efficiency of power transmission from one station to others, shortest-path Betweenness Centrality (BC) indicating the number of a station’s occurrences on the shortest paths between indirectly connected stations, Hub describing stations that are connected to a large number of important stations, and Authority indicating the stations that connect many important stations. Experimental results revealed that the Bangkok Noi station was most significant when the measures DC, CC and BC were considered and, on the other hand, that the North Bangkok station was vital in terms of CC, Hub and Authority. Therefore, these stations need to be closely monitored and their operation to be carried out with extreme care in order to prevent the occurrence of power transmission failures within the Bangkok metropolitan area.

The use of online social media is also connected with the real world. A very common example of this is the effect of social media coverage on the chances of success of elections. Previous literature has identified that the outcome of elections can often be predicted based on online public discussions. These discussions can be across various online social network with a special focus on the candidate's own accounts. Among many other forms of social media, Wikipedia is a very widely-used self-organizing information resource. The management and administration of Wikipedia is performed using special users which are elected by means of online public elections. In other words, the results of these elections pose as an emergent outcome of a large-scale self-organized opinion formation process. However, due to dynamical, and non-linear interactions besides the presence of mutual dependencies between election participants, a statistical analysis of this data can both be cumbersome as well as inefficient in terms of information extraction. We believe that social network analysis is a more appropriate alternative. It allows for the identification of local and global patterns, identification of influential nodes as well as the contacts involved in the influence. In general, this particular analytic technique can help in examining the internal complex network dynamics. In the current paper, we investigates whether personal contacts matter more than know-how contacts in wiki election nominations and voting participation. We employ the use of standard social network analysis tools such as Pajek and Gephi. The presented work demonstrates the significance of personal contacts over know-how contacts of a person in online elections. We have discovered that personal contacts, i.e. immediate neighbors (based on degree centrality) and neighborhood (k-neighbors) of a person have a positive effect on a person’s nomination as an administrator and also contribute to the active participation of voters in voting. Moreover, know-how contacts, analyzed by means of measures such as betweenness and closeness centralities, have a relatively insignificant effect on the selection of a person. However, know-how contacts, measured in terms of betweenness centrality can positively contribute only to the voting process—primarily due to the role played in passing information around the network. These contacts, also measured in terms of influence domain and PageRank, can play a vital role in the selection of an admin. Additionally, such contacts have a positive association with the voting process in terms of reachability and brokerage roles.

Food addiction (FA) is a condition in which ultra-processed foods (UPFs) activate the brain's reward pathways, leading to binge eating, loss of control, and continued consumption despite negative consequences. It can appear early in childhood and is linked to behavioral, sociodemographic, and metabolic factors. This study assessed the contribution of FA, its structure, and connectivity in relation to sociodemographic, nutritional status, and metabolic variables in network analysis. A cross-sectional study was conducted with 93 children (7-11years old) living in Vitória de Santo Antão, Brazil. FA was assessed using the Yale Food Addiction Scale for Children, which was translated and validated for the Brazilian child population. Sociodemographic (age, sex, race, socioeconomic class), anthropometric (body weight, height, waist circumference, BMI, BMI-for-age, body fat percentage, lean mass, and fat mass), and metabolic (blood pressure, total cholesterol, triglycerides, HDL, LDL, and fasting glucose) factors were analyzed. For network analysis, the degree centrality (DC), closeness centrality (CC), betweenness centrality (BC), and eigenvector centrality (EC) were evaluated. FA exhibited moderate centrality in sociodemographic and metabolic networks, acting as a connector between key variables such as age and socioeconomic class (BC = 0.071-0.500; EC = 0.301-0.500; CC = 0.636-0.667). These metrics indicate that FA, while not dominant, maintains access to influential nodes and participates in relevant information pathways. In contrast, within the anthropometric network, FA showed a peripheral role, with fewer direct links (DC = 0.222-0.285) and limited intermediation (BC = 0.111). Variation in centrality across domains underscores the selective integration of FA, suggesting that its impact is context dependent.

In 2020, the world is facing a Covid-19 virus pandemic. The fields of epidemiology and networks are needed in dealing with its spread. Individual (contact) tracing is an important control measure in the spread of infectious diseases. The network of contacts describes the potential pathways for the spread of the disease. Social Network Analysis (SNA) is the study of structural construction based on graph theory with Degree Centrality, Betweenness Centrality, and Closeness Centrality. In this paper, the SNA graph provides a description of the contact network in a resident cluster. Visualization of the formed SNA graph provides information on the pattern of contact relationships in the cluster. The measurement of centrality in the SNA method identifies who has a high value of connectedness and closeness in the cluster. The calculation of degree centrality shows that node-F391 is the node with the highest interaction, which is 3,203. This indicates that node interacts the most with residents in its environment within 14 days before and after being positive for COVID-19 based on the date of the swab test. The calculation of Betweenness Centrality shows that nodes-B041 and C371 are important actors with the highest value of 0.006. The two node becomes an important link. The use of graphs on SNA provides visualization of the contact network to distinguish potential relationships between close contacts, so that contact tracing can be carried out based on disease path mapping. Compared to the conventional way of reviewing individual contact records, graph visualization on the SNA is a good adjunct tool for healthcare workers in contact tracing investigations.

Many studies on coauthorship networks focus on network topology and network statistical mechanics. This article takes a different approach by studying micro‐level network properties with the aim of applying centrality measures to impact analysis. Using coauthorship data from 16 journals in the field of library and information science (LIS) with a time span of 20 years (1988–2007), we construct an evolving coauthorship network and calculate four centrality measures (closeness centrality, betweenness centrality, degree centrality, and PageRank) for authors in this network. We find that the four centrality measures are significantly correlated with citation counts. We also discuss the usability of centrality measures in author ranking and suggest that centrality measures can be useful indicators for impact analysis.

Information and communication technology (ICT) outsourcing to suppliers has the potential to enhance buyers’ product innovation and overall performance. However, the impact of technology outsourcing to lower-tier suppliers on buyers’ financial performance in multi-tier supply chain networks remains unclear. Unlike prior dyadic studies that focus on a buyer-tier1 supplier perspective, our study extends the analytical scope to encompass buyer, tier-1, and tier-2 ICT supplier triads using multi-tier supply network data from Capital IQ. Building on existing literature indicating that a buyer’s betweenness centrality negatively affects its financial performance, we further find that tier-1 suppliers’ betweenness centrality also exhibits a negative relationship with buyers’ financial performance. In addition, we provide empirical evidence that a buyer’s timely supplier payment amplifies the positive effect of buyer degree centrality on its financial performance, while alleviating the positive effect of tier-1 suppliers’ degree centrality on buyers’ financial performance. Interestingly, timely supplier payment negatively moderates the association between a buyer’s closeness centrality and its financial performance, but positively moderates the corresponding association between tier-1 supplier closeness centrality and buyer financial performance. Our empirical analysis advances theoretical understanding of lower-tier suppliers and sheds light on the bridging role of tier-1 suppliers in the literature of multi-tier supply chain relationships. These findings highlight the benefits of technology-enabled multi-tier supply network structures and their interactions with buyer-tier1 supplier relationships. This study also provides practical insights for firms to improve financial outcomes through strategic ICT outsourcing to multi-layer suppliers.

We investigate the use of centrality measures to determine connected dominating sets (CDSs) for mobile ad hoc networks (MANETs) whose topology changes dynamically with time. A CDS is typically considered the graph theory equivalent for the backbone of a network and has been commonly used as the underlying topology for network-wide broadcasts with minimal retransmissions (accomplished by including fewer nodes in the CDS). In the case of MANETs, the degree centrality has been so far the most commonly used measure to determine a CDS with minimum node size (number of constituent nodes in the CDS). But, degree centrality-based CDS has been observed to be quite unstable in the presence of node mobility. In the rapidly emerging area of Network Science, centrality measures such as Eigenvector centrality, Betweenness centrality and Closeness centrality are used for complex network analysis. In this paper, we explore the use of these three centrality measures as the underlying criterion for inclusion of nodes in a CDS for MANETs and evaluate the lifetime and node size of such CDSs in comparison to that incurred for the degree centrality-based CDS and the maximum stable CDS determined using a benchmarking algorithm. We observe the Eigenvector centrality-based CDS to be the most stable (but the CDS node size is also the largest); the Betweenness centrality-based CDS is the least stable (but incurs the smallest CDS node size). The Betweenness centrality-based CDS incurs the lowest values for the CDS Node Size / CDS Lifetime tradeoff ratios when most of the nodes, if not all of them, are mobile.

Human Brain can be studied and analysed using neuroimages viz. MRI(Magnetic Resonance Imaging) / fMRI(Functional Magnetic Resonance Imaging) / PET(Positron Emission Tomography) / EEG(Electroencephalography) / MEG(Magnetoencephalography), which brings out the hidden information from it. Connectomes are graph like structure which represents complex brain network connectivity which are of two type's mainly structural connectivity and functional connectivity. Connectivity can be anatomical and functional properties of the brain. Nodes are voxels or Regions of Interest whereas edges are fibre bundles, temporal correlation between regions, in structural connectome and functional connectome respectively. This work focuses on functional connectivity analysis of brain network obtained through RS- fMRI images for identification of important regions in the human brain using image processing and graph theoretical approaches. The RS-fMRI images are obtained from 1000 Functional connectomes project and preprocessed using image processing techniques. Then the image is parcellated using AAL(Automated Anatomical Labeling) atlas and binary matrix is obtained. The graph is constructed from the derived matrix that exhibits functional connectivity between ROIs(Region of Interest). The various centrality measures (degree centrality, eigenvector centrality, betweenness centrality and closeness centrality) are used to identify the ROIs that act as provincial and/or connector hubs. The prominent provincial hubs are Rolandic Operculum, Thalamus, Insula, Hippocampus, Olfactory and connector hubs are Insula, Putamen, Occipital superior gyrus, Parietal Superior gyrus and Supramarginal gyrus. This work highlights the key regions in human brain which is involved in massive communication and information flow within the network.

We present correlation analysis between the centrality values observed for nodes (a computationally lightweight metric) and the maximal clique size (a computationally hard metric) that each node is part of in complex real-world network graphs. We consider the four common centrality metrics: degree centrality (DegC), eigenvector centrality (EVC), closeness centrality (ClC) and betweenness centrality (BWC). We define the maximal clique size for a node as the size of the largest clique (in terms of the number of constituent nodes) the node is part of. The real-world network graphs studied range from regular random network graphs to scale-free network graphs. We observe that the correlation between the centrality value and the maximal clique size for a node increases with increase in the spectral radius ratio for node degree, which is a measure of the variation of the node degree in the network. We observe the degree-based centrality metrics (DegC and EVC) to be relatively better correlated with the maximal clique size compared to the shortest path-based centrality metrics (ClC and BWC).